The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of utmost importance to the doctor in reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, an MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures. CAD systems are often used to aid visual inspection by surgeons to verify the results of their interventions (e.g., reconstructive surgery). State-of-the-art image acquisition tools allow them to quickly capture a two-dimensional (2-D) computed tomographic (CT) image of the region of interest during and right after surgery using advanced C-arm scanners. However, such images typically cover only a very small field of view (FOV) and are very noisy in nature, which make it difficult for surgeons to interpret them to determine, for example, orientation of the images with respect to the captured anatomy. The various structures being imaged are only partially covered in the small FOV, and there is ambiguity as to which parts of the image represent these various structures.
Therefore, there exists a need to improve visualization to facilitate evaluation of surgical results.